PROJECT SUMMARY Non-invasive imaging of metabolic pathways in neurological disease has been a long-standing goal to monitor disease progression or therapy efficacy. Clinicians have in reality only one metabolic imaging tool, which is detection of intravenously administered 2-18F-fluoro-2-deoxy-D-glucose (FDG) with positron emission tomography (PET). Its unrivaled sensitivity makes FDG-PET a mainstay application for cancer detection outside of the brain. However, the high uptake of FDG by normal brain drastically reduces the image contrast and the usefulness of FDG-PET in studying neurological disease. 1H and 13C MR spectroscopic imaging (MRSI), and hyperpolarized 13C MRSI are promising methods but have failed to reach clinical significance due to a variety of reasons including technical complexity and lack of robustness and/or sensitivity. Substrates labeled with deuterium (2H) have been used for decades to study whole body human metabolism by detecting the 2H label in downstream metabolic products in plasma or tissue samples using mass spectroscopy or magnetic resonance spectroscopy (MRS). Our first-in-man deuterium metabolic imaging (DMI) maps of glucose metabolism in healthy brain and DMI maps showing the distribution of the ?Warburg-effect? in patients with high grade brain tumors illustrate that DMI has the potential to become a widely applicable brain imaging method with strong clinical utility. The proposal is organized around three specific aims that will establish the clinical applicability, scientific validity and reproducibility of DMI. As part of Aim 1, the 2H-based measurement of glucose metabolism will be validated with `gold-standard' 13C-based MRS on animal and human brain. The availability of affordable 2H-labeled substrates, such as glucose (and acetate), together with the relative ease of DMI data acquisition greatly expedites the translation to a clinical 3 Tesla MRI scanner. In Aim 2 we will optimize the protocol for use of DMI in the clinic, report on reproducibility of the DMI-based metabolic maps, and explore the metabolic differences detected with DMI in patients diagnosed with primary and secondary brain tumors. In the third aim we compare in patients diagnosed with brain tumors the metabolism-based image contrast observed using DMI with the image contrast detected using FDG-PET. By the completion of the proposed studies we expect to have a simple, but robust metabolic imaging modality that can provide 3D maps of the metabolic fate of multiple substrates in a wide range of neurological disorders.